People today are often overwhelmed by choice when deciding what music to which to listen. As a person is presented with more and more options, a person may find selecting appropriate music to listen to, etc. at a given time increasingly difficult. Accordingly, many people seek out tools that help them select music to which to listen.
Several methods for automation of music selection exist that people may use. Random selection (i.e., “shuffle”) is one technique, in which a random song from the person's library is selected and played. Other techniques exist that select music based on a desired attribute of a song. Many such techniques involve selecting a next piece of music based on attributes of a current-playing piece of music. When a person is listening to music, if the music has a particular attribute (e.g., is music from a particular genre), then another piece of music having that same particular attribute may be selected as the next piece of music to play. Using these attribute-identification techniques, once an attribute is identified, music having that same attribute is selected each time music is to be played.
To select a next piece of music using attribute-identification techniques, the attributes of pieces of music available for selection are analyzed to identify attributes of the pieces of music. For example, a genre (e.g., country music, music from romantic comedy movies, etc.) may be identified for the music. These attributes may be identified in various ways, including through analysis of metadata associated with the music (e.g., a data field associated with the music that lists a genre for the media) and performing data processing on the content to identify properties of content (e.g., performing an acoustical waveform analysis to identify attributes like tempo). When a piece of music available for selection is identified as having the attribute on which the selection is being conducted, the piece of music may be selected and played back.